• Journal of KIISE
• /
• v.44 no.11
• /
• pp.1236-1243
• /
• 2017

In this paper, we propose a novel way of producing keyword networks, named LSI-based ClusterTextRank, which extracts significant key words from a set of clusters with a mutual information metric, and constructs an association network using latent semantic indexing (LSI). The proposed method reduces the dimension of documents through LSI, decomposes documents into multiple clusters through k-means clustering, and expresses the words within each cluster as a maximal spanning tree graph. The significant key words are identified by evaluating their mutual information within clusters. Then, the method calculates the similarities between the extracted key words using the term-concept matrix, and the results are represented as a keyword association network. To evaluate the performance of the proposed method, we used travel-related blog data and showed that the proposed method outperforms the existing TextRank algorithm by about 14% in terms of accuracy.

• ETRI Journal
• /
• v.29 no.5
• /
• pp.545-558
• /
• 2007

This paper presents an efficient semantic service discovery scheme called UbiSearch for a large-scale ubiquitous computing environment. A semantic service discovery network in the semantic vector space is proposed where services that are semantically close to each other are mapped to nearby positions so that the similar services are registered in a cluster of resolvers. Using this mapping technique, the search space for a query is efficiently confined within a minimized cluster region while maintaining high accuracy in comparison to the centralized scheme. The proposed semantic service discovery network provides a number of novel features to evenly distribute service indexes to the resolvers and reduce the number of resolvers to visit. Our simulation study shows that UbiSearch provides good semantic searchability as compared to the centralized indexing system. At the same time, it supports scalable semantic queries with low communication overhead, balanced load distribution among resolvers for service registration and query processing, and personalized semantic matching.

• Journal of Information Management
• /
• v.40 no.1
• /
• pp.133-156
• /
• 2009

In this study, we link informal Web resources to KISTI NDSL's collections using automatic semantic indexing and tagging to examine the possibility of the service which recommends related documents using the similarity between KISTI's formal information resources and informal web resources. We collect and index Web resources and make automatic semantic linking through STEAK with KISTI's collections for NDSL retrieval. The macro precision which shows retrieval precision per a subject category is 62.6% and the micro precision which shows retrieval precision per a query is 66.9%. The experts' evaluation score is 76.7. This study shows the possibility of semantic linking NDSL retrieval results with Web information resources and expanding information services' coverage to informal information resources.

• Journal of Korea Multimedia Society
• /
• v.12 no.10
• /
• pp.1499-1507
• /
• 2009

With the abundance of digital contents, the necessity of precise indexing technology is consistently required. To meet these requirements, the intelligent software entity needs to be the subject of information retrieval and the interoperability among intelligent entities including human must be supported. In this paper, we analyze the unifying framework for multi-modality indexing that Snoek and Worring proposed. Our work investigates the method of improving the authenticity of indexing information in contents-based automated indexing techniques. It supports the creation and control of abstracted high-level indexing information through ontological concepts of Semantic Web skills. Moreover, it attempts to present the fundamental model that allows interoperability between human and machine and between machine and machine. The memory-residence model of processing ontology is inappropriate in order to take-in an enormous amount of indexing information. The use of ontology repository and inference engine is required for consistent retrieval and reasoning of logically expressed knowledge. Our work presents an experiment for storing and retrieving the designed knowledge by using the Minerva ontology repository, which demonstrates satisfied techniques and efficient requirements. At last, the efficient indexing possibility with related research is also considered.

• Journal of the Korean Society for information Management
• /
• v.1 no.1
• /
• pp.25-42
• /
• 1984

The intellectual organization of information for storage and retrieval is one of the problem areas of information science. But the most of the methods have been developed are based on the coordination of keywords by Boolean logic or traditional classification systems. It is necessary to include in indexing process not just concepts or keywords, but also to express explicitly the relationship between them. Farradane believed that such a system should be founded on the psychology of thinking and developed an outline structure for concept organization and a series of explicit relations. The system of relational operators provides nine symbols which enable the semantic relationship between any two concepts to be explicitly specified in a analet. With the addition of the permutations of such analets, formed according to simple rules, alphabetical arrangement of the first term provides a complete logical subject index. Relational indexing as the basis of an indexing language has the potential to offer improved retrieval performance.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.14 no.4
• /
• pp.35-45
• /
• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• Journal of Broadcast Engineering
• /
• v.5 no.1
• /
• pp.41-49
• /
• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• Journal of Korea Multimedia Society
• /
• v.10 no.5
• /
• pp.559-568
• /
• 2007

Anchor-person scene detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper proposes an efficient algorithm extracting anchor ranges that exist in sports news video for unit structuring of sports news. To detect anchor person scenes, first, anchor person candidate scene is decided by DCT coefficients and motion vector information in the MPEG4 compressed video. Then, from the candidate anchor scenes, image processing method is utilized to classify the news video into anchor-person scenes and non-anchor(sports) scenes. The proposed scheme achieves a mean precision and recall of 98% in the anchor-person scenes detection experiment.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.8C
• /
• pp.1133-1141
• /
• 2004

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 90 percents.

• Journal of the Korean Society for information Management
• /
• v.8 no.1
• /
• pp.78-99
• /
• 1991

So far, most of Korea's newspapers indexing have been done manually using tesaurus. In recent years, however, the need for automatic indexing system has grown stronger so as for indexers to save time, efforts and money. And some newspapers have started establishing their databases along with introducing electronic newspapers and CTS. This thesis is on establishing and automatic indexing system for the full-text of the Korea Economic Daily's articles, which have been accumulated in its database, KETEL. In my thesis, I suggest methods to create a keyword file, a stopword list, an auxiliary word list and an infected word list by applying linguistic analysis methods to Hangul, taking advantage of the language's morphological peculiarity. Through these studies, I was able to reach four conclusions as follows. First, we can obtain satisfactory keywords by automatic indexing methods that were made through morphological analysis. Second, an indexer can improve the efficiency of indexing work by controlling extracted vocabulary, as syntax analysis and semantic analysis is not complete in Hangul. Third, The keyword file in this system which is made of about 20,000 most-frequently-used newspaper terms can be used in the future in compiling a thesaurus. Finally, the suggested methods to prepare an auxiliary word list and an infected word list can be applicable to designing other automatic systems.